README.md

library_name: transformers
license: mit
language:

en
base_model: meta-llama/Llama-2-7b-hf
Model Card for Clinical Studies Instruction-Finetuned LLaMA-7B
Model Details
Model Description
This model is a fine-tuned version of the LLaMA-2-7B model from Meta, optimized using 1.5 lakh open-source clinical studies data for instruction-based tasks. The fine-tuning process has tailored the model to better understand and generate responses in the domain of clinical studies, making it particularly useful for tasks involving medical and clinical research data.

Developed by: Shudhanshu Shekhar
Model type: Instruction-finetuned LLaMA-2-7B
Language(s) (NLP): English
License: MIT
Finetuned from model: Meta's LLaMA-2-7B
Model Sources
Repository: [Link to your Hugging Face model repository]
Paper [optional]: [Link to any related paper]
Demo [optional]: [Link to a demo if available]
Uses
Direct Use
This model can be directly used for generating responses or insights from clinical studies data, facilitating tasks such as summarization, information retrieval, and instruction following in the medical and clinical research fields.

Downstream Use
The model can be further fine-tuned or integrated into applications focused on clinical decision support, medical research analysis, or healthcare-related natural language processing tasks.

Out-of-Scope Use
This model is not suitable for real-time medical diagnosis, treatment recommendations, or any other critical medical decision-making processes without human oversight. Misuse in contexts requiring precise and validated medical information could lead to incorrect or harmful outcomes.

Bias, Risks, and Limitations
While the model has been fine-tuned on clinical studies data, it may still exhibit biases present in the original data. Users should be cautious when interpreting outputs, particularly in sensitive or critical contexts such as healthcare. The model may also produce outdated or incorrect information if the underlying data is not current.

Recommendations
Users should critically evaluate the model's outputs and consider the context in which it is being used. It is advisable to have human oversight when deploying the model in healthcare or clinical environments.

How to Get Started with the Model
Use the code below to get started with the model:

python
Copy code
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("your-hf-username/clinical-llama-7b")
model = AutoModelForCausalLM.from_pretrained("your-hf-username/clinical-llama-7b")

input_text = "Summarize the following clinical study on..."
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Training Details
Training Data
The model was fine-tuned on a dataset of 1.5 lakh open-source clinical studies, focusing on enhancing its ability to follow instructions and generate informative content within the medical and clinical research domains.

Training Procedure
Training regime: Mixed precision training with fp16
Hardware: NVIDIA A100 GPUs
Training duration: Approximately [insert hours/days]
Evaluation
Testing Data, Factors & Metrics
Testing Data
The model was evaluated on a subset of clinical studies data that was not seen during training.

Metrics
The evaluation focused on accuracy in following instructions, the relevance of generated responses, and the preservation of key medical facts.

Results
The fine-tuned model demonstrated improved performance in generating accurate and contextually relevant responses in the domain of clinical studies, outperforming the base model in instruction-following tasks.

Environmental Impact
Hardware Type: NVIDIA A100 GPUs
Hours used: [insert hours]
Cloud Provider: [insert cloud provider if applicable]
Compute Region: [insert compute region]
Carbon Emitted: [insert carbon emissions if known]
Technical Specifications
Model Architecture and Objective
The model architecture is based on Meta's LLaMA-2-7B, optimized for natural language processing tasks, with a focus on clinical studies data.

Compute Infrastructure
Hardware
Training was conducted on NVIDIA A100 GPUs.

Software
The fine-tuning was performed using the PyTorch framework with the Hugging Face Transformers library.

Citation
If you use this model in your research, please cite:

bibtex
Copy code
@article{shekhar2024clinicalllama,
  title={Clinical Studies Instruction-Finetuned LLaMA-7B},
  author={Shudhanshu Shekhar},
  year={2024},
  howpublished={\url{https://huggingface.co/your-hf-username/clinical-llama-7b}},
}
Model Card Authors
Shudhanshu Shekhar